Developing a Decision Support Tool for Increased Warehouse Picking Efficiency

Abstract: Problem description: Warehousing is central in order to achieve a competitive supply chain, and considered essential for the success, or failure, of businesses today. In general, warehouses account for a large share of the company logistics costs. Consequently, there is a need for warehouses to operate smoother, faster and more accurate. Within warehousing, the most labor-intensive and costly warehouse operation is order picking, this is mainly due to the large amount of travelling involved. All articles included in this study agree that order picking account for at least 50 percent of warehouses' total operating costs. Warehouses thus carry great potential to justify the expenses they bring through reducing the time spent on activities that are not value adding. Purpose: The overall goal in this thesis is to provide guidance for how a warehouse can operate more efficient by improving its picking performance, which also includes reviewing the closely interlinked warehouse operations storage allocation and routing. Research questions: How can a decision support tool for reviewing the choices of storage allocation, order picking, and routing methods in manual warehouse operations be put together in a structured way? Which features should be considered in the decision support tool for choosing methods for improving warehouse operations? Methodology: The guidance for improved picking performance was framed into a decision support tool building on a thorough review and analysis of the research available in the area. A case study on picking efficiency was conducted in order to create a deep understanding for the issues and challenges that prevail in warehousing, and also to ensure that the final recommendations and the answers to the research questions have good support in academia. Once the tool was created, an illustrative example was used to demonstrate the use of the tool on a more detailed level and to test its comprehensibility and usability. Conclusions: In many areas, the resulting tool manages to provide unequivocal guidance for how to improve a warehouse’ picking operations. Multiple features are identified as important for the decision process; among those are demand skewness, seasonality among different SKUs, total demand variation and pick list size. Company objectives and priorities were also identified as a central feature due to the interrelatedness of the decisions connected to picking and their well-known tradeoffs. The research is however sometimes scarce, and further studies need to be carried-out in order to complement and level the strength of the tool.